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RADIUS: Ranking, Distribution, and Significance - A Comprehensive Alignment Suite for Survey Simulation

Weronika Łajewska, Paul Missault, George Davidson, Saab Mansour · Mar 19, 2026 · Citations: 0

Automatic Metrics General Simulation Env
Open arXiv Find Implementation RSS feed Shortlist (0)

How to use this page

Low trust

Use this as background context only. Do not make protocol decisions from this page alone.

Best use

Background context only

What to verify

Validate the evaluation procedure and quality controls in the full paper before operational use.

Evidence quality

Low

Derived from extracted protocol signals and abstract evidence.

Abstract

Simulation of surveys using LLMs is emerging as a powerful application for generating human-like responses at scale. Prior work evaluates survey simulation using metrics borrowed from other domains, which are often ad hoc, fragmented, and non-standardized, leading to results that are difficult to compare. Moreover, existing metrics focus mainly on accuracy or distributional measures, overlooking the critical dimension of ranking alignment. In practice, a simulation can achieve high accuracy while still failing to capture the option most preferred by humans - a distinction that is critical in decision-making applications. We introduce RADIUS, a comprehensive two-dimensional alignment suite for survey simulation that captures: 1) RAnking alignment and 2) DIstribUtion alignment, each complemented by statistical Significance testing. RADIUS highlights the limitations of existing metrics, enables more meaningful evaluation of survey simulation, and provides an open-source implementation for reproducible and comparable assessment.

Low-signal caution for protocol decisions

Use this page for context, then validate protocol choices against stronger HFEPX references before implementation decisions.

  • The available metadata is too thin to trust this as a primary source.
Human Eval Hub LLM-as-Judge Hub Pairwise Preference Hub

Should You Rely On This Paper?

This paper is adjacent to HFEPX scope and is best used for background context, not as a primary protocol reference.

Best use

Background context only

Use if you need

A secondary eval reference to pair with stronger protocol papers.

Main weakness

The available metadata is too thin to trust this as a primary source.

Trust level

Low

Usefulness score

37/100 • Low

Treat as adjacent context, not a core eval-method reference.

Human Feedback Signal

Not explicit in abstract metadata

Evaluation Signal

Detected

Usefulness for eval research

Adjacent candidate

Extraction confidence 45%

If you are doing eval pipeline work, start here:

Human Eval Hub LLM-as-Judge Hub Pairwise Preference Hub Tool-Use Eval Hub

What We Could Verify

These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.

Human Feedback Types

missing

None explicit

No explicit feedback protocol extracted.

"Simulation of surveys using LLMs is emerging as a powerful application for generating human-like responses at scale."

Evaluation Modes

partial

Automatic Metrics, Simulation Env

Includes extracted eval setup.

"Simulation of surveys using LLMs is emerging as a powerful application for generating human-like responses at scale."

Quality Controls

missing

Not reported

No explicit QC controls found.

"Simulation of surveys using LLMs is emerging as a powerful application for generating human-like responses at scale."

Benchmarks / Datasets

missing

Not extracted

No benchmark anchors detected.

"Simulation of surveys using LLMs is emerging as a powerful application for generating human-like responses at scale."

Reported Metrics

partial

Accuracy

Useful for evaluation criteria comparison.

"Moreover, existing metrics focus mainly on accuracy or distributional measures, overlooking the critical dimension of ranking alignment."

Human Feedback Details

  • Uses human feedback: No
  • Feedback types: None
  • Rater population: Not reported
  • Unit of annotation: Ranking (inferred)
  • Expertise required: General

Evaluation Details

  • Evaluation modes: Automatic Metrics, Simulation Env
  • Agentic eval: None
  • Quality controls: Not reported
  • Evidence quality: Low
  • Use this page as: Background context only

Protocol And Measurement Signals

Benchmarks / Datasets

No benchmark or dataset names were extracted from the available abstract.

Reported Metrics

accuracy

Research Brief

Metadata summary

Simulation of surveys using LLMs is emerging as a powerful application for generating human-like responses at scale.

Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.

Key Takeaways

  • Simulation of surveys using LLMs is emerging as a powerful application for generating human-like responses at scale.
  • Prior work evaluates survey simulation using metrics borrowed from other domains, which are often ad hoc, fragmented, and non-standardized, leading to results that are difficult to compare.
  • Moreover, existing metrics focus mainly on accuracy or distributional measures, overlooking the critical dimension of ranking alignment.

Researcher Actions

  • Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
  • Validate inferred eval signals (Automatic metrics, Simulation environment) against the full paper.
  • Use related-paper links to find stronger protocol-specific references.

Caveats

  • Generated from abstract + metadata only; no PDF parsing.
  • Signals below are heuristic and may miss details reported outside the abstract.

Recommended Queries

Accuracy

Research Summary

Contribution Summary

  • Simulation of surveys using LLMs is emerging as a powerful application for generating human-like responses at scale.
  • In practice, a simulation can achieve high accuracy while still failing to capture the option most preferred by humans - a distinction that is critical in decision-making applications.
  • We introduce RADIUS, a comprehensive two-dimensional alignment suite for survey simulation that captures: 1) RAnking alignment and 2) DIstribUtion alignment, each complemented by statistical Significance testing.

Why It Matters For Eval

  • Simulation of surveys using LLMs is emerging as a powerful application for generating human-like responses at scale.
  • In practice, a simulation can achieve high accuracy while still failing to capture the option most preferred by humans - a distinction that is critical in decision-making applications.

Researcher Checklist

  • Gap: Human feedback protocol is explicit

    No explicit human feedback protocol detected.

  • Pass: Evaluation mode is explicit

    Detected: Automatic Metrics, Simulation Env

  • Gap: Quality control reporting appears

    No calibration/adjudication/IAA control explicitly detected.

  • Gap: Benchmark or dataset anchors are present

    No benchmark/dataset anchor extracted from abstract.

  • Pass: Metric reporting is present

    Detected: accuracy

Related Papers

Papers are ranked by protocol overlap, extraction signal alignment, and semantic proximity.

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